Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively communicates that this is a diagnostic/verification tool (not a data retrieval tool), describes its output ('Authentication status and next steps'), and provides context about when it should be used in troubleshooting workflows. However, it doesn't explicitly mention whether this tool makes any network calls or has side effects, leaving some behavioral aspects implicit.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description uses a well-structured format with clear semantic tags (<purpose>, <when_to_use>, etc.) that makes information easy to parse. Each section is front-loaded with the most important information, and there's no wasted text - every sentence serves a specific purpose in guiding the agent's understanding of when and how to use this diagnostic tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that this is a parameterless diagnostic tool with an output schema (which handles return value documentation), the description provides excellent contextual completeness. It covers purpose, usage scenarios, workflow positioning, and next steps - everything an agent needs to understand when and why to invoke this tool. The structured format ensures all relevant context is present without redundancy.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters with 100% schema description coverage, so the baseline would be 4. The description doesn't need to explain parameters, and it correctly doesn't attempt to do so. The structured format efficiently communicates that this is a parameterless diagnostic check without unnecessary elaboration.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose with a specific verb ('Verify') and resource ('Polarion authentication and connection status') in the <purpose> tag. It clearly distinguishes this diagnostic tool from its sibling tools that perform data retrieval or configuration operations, making it immediately clear what this tool does differently.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool through the <when_to_use> list and <workflow_position> section, which includes specific scenarios like authentication errors, setup verification, and debugging. It also references alternative tools (open_polarion_login, set_polarion_token, get_polarion_projects) in the <next_steps> section, giving clear direction on what to do instead when this tool reveals issues.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a read operation (implied by 'access'), requires exact case-sensitive inputs, and includes troubleshooting tips for errors like 404s. However, it lacks details on rate limits, authentication needs, or pagination, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections, but it is verbose with redundant information (e.g., repeating parameter details in multiple sections). Some sentences could be condensed, such as merging examples and critical requirements, to improve efficiency without losing clarity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (4 parameters, no annotations, 0% schema coverage) and the presence of an output schema, the description is highly complete. It covers purpose, usage, parameters, examples, troubleshooting, and notes, providing all necessary context for an agent to use the tool effectively without needing to explain return values.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, so the description must fully compensate. It adds significant meaning beyond the schema by explaining each parameter's purpose (e.g., project_id from get_polarion_projects(), space_id as exact name, document_name examples), providing examples, and noting critical requirements like case-sensitivity and quoting rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Access specific structured documents within a Polarion space' and distinguishes it from siblings by specifying it's for documents (not projects or work items). The examples further clarify it retrieves documents like specifications and manuals, making the purpose specific and differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (e.g., when user provides space and document names, for accessing curated requirement collections) and when not to use it (suggesting get_polarion_work_items() as an alternative that might provide better information). The workflow_position section outlines a step-by-step process and explicitly names alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's function (retrieving detailed project information), specifies that it's optional in workflows, and notes it's rarely needed for most tasks. However, it doesn't explicitly mention whether this is a read-only operation (though implied by 'Get'), potential rate limits, or authentication requirements, leaving some behavioral aspects uncovered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear XML-like tags (<purpose>, <when_to_use>, etc.) that make it easy to parse. Each section is front-loaded with essential information, and every sentence earns its place by providing specific guidance or clarification without unnecessary verbosity. The bullet points enhance readability while maintaining brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is complete enough. It covers purpose, usage guidelines, workflow positioning, parameter semantics, and includes a helpful note. With an output schema present, the description appropriately doesn't need to explain return values, focusing instead on when and how to use the tool effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate fully. It does so by explaining both parameters: project_id ('Exact project ID from get_polarion_projects() results') and fields ('@basic for essential info, @all for complete details'), adding crucial meaning beyond the bare schema. This provides clear semantic context for each parameter's purpose and usage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as 'Get detailed information about a specific Polarion project' with specific verb ('Get') and resource ('Polarion project'), and distinguishes it from siblings like get_polarion_projects (which lists projects) and get_polarion_work_items (which focuses on work items). The <purpose> tag explicitly articulates this distinct function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use ('When you need detailed project metadata', 'After using get_polarion_projects()') and when not to use ('RARELY needed for most exploration tasks', 'USUALLY SKIP: Most tasks should go directly to get_polarion_work_items()'). It names alternatives (get_polarion_projects, get_polarion_work_items) and positions the tool in a workflow, offering comprehensive usage context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's role in authentication verification, its position as the first step in exploration workflows, and its output usage (project 'id' field for subsequent calls). However, it doesn't mention potential rate limits, error conditions, or pagination behavior, which keeps it from a perfect score.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear XML-like tags (<purpose>, <when_to_use>, etc.) that make it easy to parse. Each section is front-loaded with essential information, and every sentence adds value—from purpose statements to practical examples. There is no redundant or verbose content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (1 optional parameter) and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage guidelines, workflow positioning, parameter semantics, examples, and output usage. No significant gaps remain for effective agent operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for its single parameter (limit), but the description compensates by explaining the parameter's purpose ('Number of projects to retrieve'), default value ('default 10'), and usage guidance ('increase for comprehensive view'). It adds meaningful context beyond the schema's basic type information, though it doesn't specify maximum allowed values or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the purpose as 'Discover available Polarion projects for exploration' with a clear verb ('Discover') and resource ('Polarion projects'). It distinguishes from siblings like get_polarion_project (singular) by emphasizing discovery of multiple projects, and from get_polarion_work_items by focusing on projects rather than work items.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'ALWAYS use this FIRST when starting Polarion exploration', 'When you need to find the correct project_id for other operations', 'When user asks about projects without specifying project name', and 'To verify authentication is working'. It also positions this tool in a workflow with specific steps, clearly differentiating it from sibling tools like get_polarion_work_items.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it retrieves detailed information (implying read-only), specifies that it should be used 'sparingly' (hinting at potential rate limits or performance considerations), and outlines output details. However, it doesn't explicitly state authentication requirements or error handling, leaving some gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (<purpose>, <when_to_use>, etc.), making it easy to parse. However, it includes some redundancy (e.g., <output> details could be inferred from the purpose) and is slightly verbose, though every section adds value. It's front-loaded with key information in the <purpose> and <when_to_use> sections.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, no schema descriptions, no annotations, but with an output schema), the description is highly complete. It covers purpose, usage guidelines, workflow integration, parameter semantics, examples, output details, and notes. The presence of an output schema means the description doesn't need to explain return values in depth, and it effectively fills all other gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, so the description must compensate fully. The <parameters> section adds significant meaning beyond the schema: it explains that project_id must match previous searches, work_item_id comes from get_polarion_work_items results, and fields has specific values ('@basic' for essential info, '@all' for complete details). This provides crucial context not in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the purpose with a specific verb ('Get detailed information') and resource ('about a specific work item'), distinguishing it from sibling tools like get_polarion_work_items (which lists items) and get_polarion_document (which focuses on documents). The <purpose> tag clearly articulates the tool's function without redundancy.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The <when_to_use> section provides explicit guidance on when to use this tool (e.g., after get_polarion_work_items, for complete details) and when not to (implied by suggesting alternatives like get_polarion_work_items for discovery). The <workflow_position> and <note> sections further clarify its role in a multi-step process, making usage context very clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and excels by disclosing behavioral traits beyond basic functionality. It explains output characteristics ('Minimal fields', 'Contains rich information'), workflow integration ('STEP 1: After get_polarion_projects()'), and practical usage notes ('This tool often contains all the information you need', 'Check results thoroughly before seeking additional tools'), providing comprehensive context for the agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with labeled sections (<purpose>, <when_to_use>, etc.), making it easy to parse. While comprehensive, it is appropriately sized with each sentence adding value (e.g., examples, workflow steps). A slight deduction as it could be more front-loaded, but overall efficient and organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, no annotations, but with output schema), the description is complete. It covers purpose, usage, parameters, examples, output details, and critical notes, providing all necessary context for an agent to select and invoke the tool correctly. The output schema existence reduces the need to explain return values, and the description fills all other gaps effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, but the description compensates fully with a detailed <parameters> section. It explains each parameter's role (e.g., 'project_id: Required. Get from get_polarion_projects() results'), provides usage tips ('limit: Use 30-50 for comprehensive searches'), and gives concrete query examples with syntax explanations, adding significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the purpose as 'Discover and search work items (requirements, tasks, etc.) in a Polarion project' with a clear verb+resource combination. It distinguishes from sibling tools like get_polarion_work_item() for detailed info and get_polarion_document() for specific documents, providing clear differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance with sections like <when_to_use> listing specific scenarios (e.g., 'MAIN DISCOVERY TOOL', 'searching for specific topics'), <workflow_position> detailing steps and alternatives (e.g., 'BEFORE using get_polarion_work_item()'), and <critical_note> advising when to use this tool versus others. It clearly defines when and how to use this tool relative to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior as opening a browser page for manual authentication, implying it's a read-only initiation step (not destructive) and part of a multi-step process. However, it doesn't specify potential side effects like browser pop-ups or system dependencies, leaving minor gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with labeled sections (<purpose>, <when_to_use>, etc.), making it easy to parse. Each sentence earns its place by providing essential information without redundancy, such as the purpose, usage conditions, workflow steps, and output description, all in a compact format.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple browser opener with no parameters), the description is complete. It covers purpose, usage, workflow integration, and output, and with an output schema present, it doesn't need to detail return values. This adequately supports an AI agent in selecting and invoking the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by clarifying that this tool requires no inputs, as it's solely for launching a browser page, which aligns with the empty schema and provides useful context beyond the structured data.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool's purpose with a specific verb ('Open') and resource ('Polarion login page'), clearly distinguishing it from sibling tools like 'set_polarion_token' or 'check_polarion_status' by focusing on browser-based manual authentication initiation rather than token management or status checking.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (e.g., 'for the first time', 'when existing token has expired', 'when check_polarion_status() shows no valid token') and integrates it into a clear workflow with named alternatives (e.g., 'set_polarion_token()', 'check_polarion_status()'), leaving no ambiguity about its role versus siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by detailing the 4-step workflow including automatic GitHub detection, fetching FRESH requirements, code analysis, and identifying implemented vs missing requirements. It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial behavioral context for a complex analysis tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The structured format with XML-like tags is well-organized and front-loaded with purpose. While somewhat verbose, each section earns its place by providing distinct value. The workflow_position section could be more concise, but overall the structure enhances clarity without significant waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex 3-parameter analysis tool with no annotations but with output schema, the description provides comprehensive context including purpose, usage guidelines, detailed workflow, and parameter semantics. The output section is minimal but acceptable since an output schema exists. It covers the essential aspects needed for effective tool selection and use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by providing detailed parameter explanations in the <parameters> section with examples for each parameter (e.g., 'AutoCar', 'HMI', 'hmi'). It clarifies that github_folder is optional with empty meaning 'analyze entire repository', adding crucial context beyond basic schema types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'Smart requirements coverage analysis between Polarion and connected GitHub repository' with specific verbs like 'verify', 'analyze', and 'identify'. It distinguishes itself from sibling tools (which are mostly getters/setters) by focusing on cross-system analysis rather than simple Polarion data retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The <when_to_use> section provides explicit scenarios including 'verify if requirements are implemented', 'gap analysis', and 'requirements traceability'. It clearly positions this as an 'INTELLIGENT COVERAGE ANALYSIS TOOL' for end-to-end verification, distinguishing it from simpler Polarion data access tools in the sibling list.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's role in configuring/storing a token (implying a write operation), specifies prerequisites (manual token generation), and mentions output confirmation. However, it lacks details on error handling, token format validation, or storage persistence.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear tags (<purpose>, <when_to_use>, etc.), making it easy to parse. Each section is front-loaded and concise, with no redundant information. The bullet points and stepwise workflow enhance readability without unnecessary verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (1 parameter, no annotations, but with output schema), the description is complete. It covers purpose, usage guidelines, workflow context, parameter semantics, and output expectations. The presence of an output schema means the description doesn't need to detail return values, and it adequately addresses all other aspects for effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema description coverage is 0%, but the description compensates well by explaining the 'token' parameter in the <parameters> section as 'The bearer token generated from Polarion's user token page,' adding meaningful context beyond the schema's basic type definition. It clarifies the token's origin and type, though it doesn't specify format constraints like length or encoding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the purpose with a specific verb ('Set') and resource ('Polarion access token'), and distinguishes it from siblings by mentioning it's used after token generation from open_polarion_login(). The <purpose> tag clearly defines the action and context.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool (after open_polarion_login() and manual token generation, with a valid token) and when not to use it (implied: not for initial login or token generation). It also outlines workflow steps and alternatives like check_polarion_status() and get_polarion_projects() in the <workflow_position> section.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.